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Record W4384914468 · doi:10.1504/ijvsmt.2023.132314

Non-pneumatic tyre-road interaction using finite element analysis

2023· article· en· W4384914468 on OpenAlexaff
Charanpreet Singh Sidhu, Zeinab El Sayegh, Moustafa El Gindy

Bibliographic record

VenueInternational Journal of Vehicle Systems Modelling and Testing · 2023
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsFinite element methodEngineeringStiffnessAutomotive engineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The purpose of this research is to identify the important characteristics of the non-pneumatic tyre by relating the structural stiffness of the wheel to the contact conditions. Based on experimental and published data, the non-pneumatic tyre model will be validated under different conditions. A successful outcome of this research would increase the efficiency of tyre design while providing a better understanding of non-pneumatic tyre behavior under different contact conditions. In this study, finite element analysis (FEA) was utilised to develop a non-pneumatic tyre-road interaction model. An analytical model of non-pneumatic tyre was then validated in static and dynamic response using several virtual tests. The validated non-pneumatic tyre model was then used to evaluate the tyre-road interaction characteristics using a rolling resistance test under different operating conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.314
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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